Methods and systems for tracking an object image by correlating sequential image frames are known. Correlation tracking requires no a-priori model, or template, and can therefore track movement of an arbitrary object image in scenery over a succession of image frames. Correlation trackers can “walk-off” an object in situations where a human operator would have no trouble identifying an object's true location, and can lose the ability to track the object image in succeeding frames.
A typical correlation tracking operation establishes a reference image which includes, for example, an image of an object in its entirety. In succeeding frames, the correlation tracker tries to compare the previously received reference image to a currently received input image by sliding the reference image around a designated search area of the input image. A summation output, taken over the search area can be produced as a metric surface, wherein a peak (i.e., minimum difference) can be used to indicate a best match between a location of the reference image over the search area to identify the most likely matching location of the object image.
When a best match is determined for a given search area, the system can attempt to quantify the quality of the match. For example, using the detected peak of the metric surface, a motion vector can be determined. The peak can be compared against a threshold criteria and, if the peak satisfies the criteria, the correlation can be considered sufficient to allow the input image to be used in updating the reference image. Reference image update strategies are known which involve blending some percentage of an aligned input image with the reference image. If the peak does not satisfy the threshold criteria, the blending operation can be dropped so that an undesirable input image will not contaminate the reference image.
Known correlation tracking techniques are typically not gain and level invariant, and can exhibit sensitivity to variations in target size, target rotation, partial obscuration and noise. This sensitivity can lead to walk-off and/or loss of track. With walk-off, a tracker remains confident that it is properly tracking an object even through the object has been lost.
Walk-off occurs gradually due, for example, to a contamination of a reference image whereby the correlation tracker incorrectly identifies what it considers to be an accurate location of an object image within the input image. In this case, the threshold criteria does not detect the contamination, and walk-off can occur. A loss of track can occur when a tracker realizes that it is no longer able to track a desired object. This occurs when the tracker is unable to identify any acceptable match within a search area.